Full‐waveform based microseismic event detection and signal enhancement: An application of the subspace approach

Abstract

Microseismic monitoring has proven invaluable for optimizing hydraulic fracturing stimulations and monitoring reservoir changes. The signal to noise ratio of the recorded microseismic data varies enormously from one dataset to another, and it can often be very low, especially for surface monitoring scenarios. Moreover, the data are often contaminated by correlated noises such as borehole waves in the downhole monitoring case. These issues pose a significant challenge for microseismic event detection. In addition, for downhole monitoring, the location of microseismic events relies on the accurate polarization analysis of the often weak P‐wave to determine the event azimuth. Therefore, enhancing the microseismic signal, especially the low signal to noise ratio P‐wave data, has become an important task. In this study, a statistical approach based on the binary hypothesis test is developed to detect the weak events embedded in high noise. The method constructs a vector space, known as the signal subspace, from previously detected events to represent similar, yet significantly variable microseismic signals from specific source regions. Empirical procedures are presented for building the signal subspace from clusters of events. The distribution of the detection statistics is analysed to determine the parameters of the subspace detector including the signal subspace dimension and detection threshold. The effect of correlated noise is corrected in the statistical analysis. The subspace design and detection approach is illustrated on a dual‐array hydrofracture monitoring dataset. The comparison between the subspace approach, array correlation method, and array short‐time average/long‐time average detector is performed on the data from the far monitoring well. It is shown that, at the same expected false alarm rate, the subspace detector gives fewer false alarms than the array short‐time average/long‐time average detector and more event detections than the array correlation detector. The additionally detected events from the subspace detector are further validated using the data from the nearby monitoring well. The comparison demonstrates the potential benefit of using the subspace approach to improve the microseismic viewing distance. Following event detection, a novel method based on subspace projection is proposed to enhance weak microseismic signals. Examples on field data are presented, indicating the effectiveness of this subspace‐projection‐based signal enhancement procedure.

Spatiotemporal evolution of the 2011 Prague, Oklahoma, aftershock sequence revealed using subspace detection and relocation

“…Subspace detectors improve upon simple cross correlation or matched filtering techniques by using multiple orthogonal waveform templates that approximately span the signals from all previously identified events within a data set; subspace detectors are also typically more computationally efficient [ Harris , ]. The subspace methodology has been increasingly used for the characterization of large earthquake swarms [ Harris , ; Morton , ; Harris and Dodge , ; Barrett and Beroza , ]; low‐frequency earthquakes within nonvolcanic tremor [ Maceira et al , ]; extensive aftershock sequences [ Harris and Dodge , ]; microseismic monitoring of hydrofracturing sequences [ Song et al , ]; exploration of deep, long‐period magmatic events [ McMahon et al , ]; characterization of coal mine‐related seismicity [ Chambers et al , ]; and investigation of induced seismicity clusters [ Benz et al , ; Skoumal et al , ].…”

Full Waveform Seismological Advances for Microseismic Monitoring

“…The singular vectors, typically referred as the signal subspace, are linearly combined to match a new signal, which can differ from those previously recorded. Further details on the generation and validation of the signal subspace can be found in Song, Warpinski, Toks€ oz, and Sadi Kuleli (2014). In this way, the subspace detection method overcomes the strongest limitation of the correlation-based detection approach, where only signals similar to the reference one can be recognized.…”

“…Another application of the subspace detection method has been recently presented in Barrett and Beroza (2014), where this method is discussed for detecting weak aftershocks. Finally, Song et al (2014) discussed the potential of this technique for hydrofracture monitoring, proving a valuable contribution for future full waveform-based microseismicity monitoring. This study discussed the good performance of the subspace approach to detect weak signals in presence of noisy data, and compared it to the results produced by standard array detectors (the monitoring network in this work consistent of two borehole installations, at different distances from the microseismicity focal region).…”

Microseismic events enhancement and detection in sensor arrays using autocorrelation‐based filtering

“…In fact, the majority of microseismic events induced by hydraulic fracturing have a typical moment magnitude (Song et al . ).…”